Abstract
Bulk modulus has wide applications in well engineering, seismic exploration, waste reinjection, and predicting pore pressure in carbonate reservoirs. However, there is no easy way to obtain accurate values for the effective bulk modulus of rocks. Practically, researchers use rigorous, costly, and time-consuming experiments on core samples. But, stress release and changing rock’s environment have affected the accuracy of results. Also, it is impossible to get accurate values of the effective bulk modulus from theory without accounting for the deformation of microcracks in the rock. Existing models do not consider the presence of microcracks because of the inability to define the positions of cracks relative to one another. Thus, earlier studies introduced approximations to define the upper and lower bounds of values. This study aims to overcome this limitation by accounting for the fluids in the microcracks, apart from those in stiff pores. From the product of the surface area and thickness of the fluid in the microcracks, the authors generated proportionality between the volume of fluid and that of the grain and obtained expression for the crack porosity. Then analytical and numerical techniques were applied to obtain models for the effective bulk modulus. The results show that the presence and magnitude of inclusions reduce the effective bulk modulus significantly. This was validated by a finite element analysis (FEA) using the FEATool run in matlab. In addition, higher volume of fluids in the microcracks makes the rate of change of the bulk modulus with the porosity to be higher.
Introduction
Rocks are composites comprising a solid matrix and fluid-filled pore spaces [1–8] that combine to produce effective elastic properties. One such elastic property is the bulk modulus. This bulk modulus is a parameter that is incorporated into geomechanics, particularly in planning and managing well engineering projects. It is also important when carrying out geophysical explorations with seismic data [9], and it defines how the volume of porous rocks changes with external pressure, e.g., during the pressurization of an oil wellbore. This parameter is useful during drilling cuttings and wastewater reinjection [1] for the safe disposal of hazardous materials derived from drilling activities. The study can extend its application to the prediction of the bulk modulus of glassy/crystalline materials that possess porosity. Furthermore, it can find application in predicting pore pressures in carbonate rocks because of the relationship between bulk compressibility (inverse of the bulk modulus) and effective pressure. The bulk modulus can be determined statically using experiments on core samples or dynamically using data from wireline logs plus theoretical equations [10–12]. Engineers and scientists mostly use and rely upon the static bulk modulus since it represents exact measurements. However, the experimental procedure is very costly to carry out (time-consuming and rigorous for some rocks, particularly shale or shaly rocks) and may yield inaccurate results if the experimenter does not ensure proper sample preservation. Changing the sample from its environment of stress (or stress release) can contribute to poor results from the experiments. The values of the individual components of the rock can differ significantly from that of the macroscopic (or bulk) frame depending on critical parameters related to geological factors in-situ.
There is currently the need to establish suitable ways of estimating the bulk modulus, especially when data for its components are available. For example, the conventional layered-rock approach that attempts to generate predictive models does not accurately simulate the static process. During experimental runs, there will be closure and/or the initiation of microcracks, which the conventional models generally do not consider. If microcracks are present in-situ, there will be fluids inside them plus those in the pores of the rock. The conventional models do not account for the extra fluids in microcracks. And this study provides a way to overcome this limitation.
The basis for defining the average property of a mixture can be in terms of conductivity, molar fraction, mass fraction, weight fraction, or volumetric fraction. And the weight fraction of a species may not be equal in magnitude to the volumetric fraction since they do not represent the same quantity. These bases are applicable in the theory of the effective medium, which describes analytically the bulk behavior of composites. The effective medium approximation uses theories to estimate the physical properties of heterogeneous materials from their components. Belyaev and Tyurnev [13] used the theory to predict the electrodynamic properties of a dielectric medium composed of metallic nanoparticles. David and Zimmerman [14] applied the effective medium theory to analyze the properties of porous rocks. Finally, Wang and Pan [15] used it to predict the physical properties of complex multiphase materials. Thus, one can relate the volumetric contribution of the components to the bulk modulus. However, the conventional methods assume that the volumetric fraction of fluids is equivalent to the porosity of the components without considering the fluids inside microcracks. It is interesting to uncover accurate ways to estimate the bulk modulus of reservoir rocks using porosity and the elastic properties of the constituents. Apart from the fluids in the pores, some fluids exist inside microcracks. This is of much interest because one cannot estimate correctly the elastic moduli of rocks without knowledge of the geometric distribution of the constituents, including those of microcracks [4,16]. To this end, an upper bound and a lower bound were established for the bulk modulus whereby subsequent models are expected to yield results between the limits. It can invoke some level of curiosity when one considers the accuracy of the upper and lower bounds themselves, especially when such bounds do not incorporate the contribution of microcracks apart from assuming a linear combination of the layers. Accounting for the fluids in the microcracks can help to overcome the challenge of not defining the deformation of the microcracks in the stress-strain approach. Thus, in this study, the fluids in the microcracks contribute to the total fluids in the rock. This is to improve the results of the effective medium approach for predicting bulk modulus. The study considers how the bulk modulus changes with porosity, and strives to improve the gaps in models used to predict the effective bulk modulus.
The Effective Medium for Layered Rocks
Some Conventional Methods Developed for the Bulk Modulus
Methodology
To pursue the numerical approach, the average bulk modulus of the conventional models was used to find a root for the parameter following the Newton–Raphson algorithm [23] and the LP Simple Engine of the Excel Solver. The conventional models selected for the averaging include the Voigt, Reuss, V–R–H, and HS models. While the initial guess was obtained graphically. In addition, the finite element analysis (FEA) was used to validate the results using properties from a selected wellbore. To do this, an elemental 3D volume representative of the size of the rock was selected and gridded using the FEATool. This tool runs on matlab. Then a model for predicting the bulk modulus of the grids was developed and a computer experiment was run to obtain the strains and stresses from the in-built linear elasticity program. The basis for the developed model is the strain energy per volume needed to compress the grids frame by the application of external load. A force of 1000 N was applied to the faces of the element, and the force divided by area gave the magnitude of the external load. This load served as the boundary condition on the faces of the element.
Data from the Tertiary basin of the Agbada formation in the Niger Delta were extracted to aid the analysis. To get the porosity at the interval, the authors applied the Raymer–Hunt–Gardner equation using the data from the transit and mineral [1]. While the data from gamma-ray aided in obtaining the Poisson’s ratio for the Tertiary formation.
Model Development for the Bulk Modulus of the Composite Rocks
The dimensionless parameter a = V−(1/3)Kc relates the volume to the proportionality constant of Eq. (20). With the use of the root-finding tool, one can use a set of local or regional data to find the value of the parameter that makes the function F(a) equal to zero. Experts use numerical methods (NM) to easily find the values of function/parameters that may be very complex, rigorous, or hard to solve. However, one cannot compare NM to the elegance of analytical solutions, especially when such solutions are obtainable. Numerical analysis should not be used to make generalizations in the petroleum industry where geologic settings can vary significantly from one region to another. The exception is when such generalization is localized.
From Eq. (23), the expression for the total or modified volume of fluids becomes the following:
Analysis and Discussions
This section evaluates the effective bulk modulus of a section of the Nembe Well-X in the Tertiary basin of the Niger Delta. This wellbore is likely a future candidate for drill cuttings reinjection, and Fig. 1 shows the data of the wireline log between 5500 ft (1676.95 m) and 6300 ft (1920.87 m). Based on the transit time in the wellbore with a value of 73.4 µs/ft (2640.85 µs/m) and sandstone at 6070 ft (1850.74 m), the porosity is 0.163 using the Raymer–Hunt–Gardner (RHG) model [1]. The bulk modulus of the solid matrix is 5.076 MPsi (35,000 MPa) and that of the fluid is 0.29 MPsi (2000 MPa). Since the problem is a one-unit layer comprising only sandstone, Eq. (28) is applicable. The effective bulk modulus gives a value of 3.7329 MPsi (25,739.05 MPa), which tends towards the bulk modulus of the solid. Figure 2 shows the variation of the bulk modulus with porosity, which indicates an inverse relationship consistent with earlier studies [4,14,19]. Using the random number generator of MS Excel, 50 data points for porosity were created from zero to one and applied to Eq. (28). The trend satisfies the conventional boundary conditions when porosity ranges from zero to one. At porosity equal to one, the value of the effective bulk modulus equals that of the fluid, while at porosity equal to zero, the value equals that of the solid. When the rock is viewed as a unit, the porosity then behaves as an inclusion that reduces the bulk modulus of a solid framework as its concentration increases. Fjær et al. [4] confirmed that the presence of microcracks reduces core samples’ stiffness. These cracks are inclusions into the rocks and so, they reduce the elastic properties of the rock. If the conventional models for deformation take the microcracks into account, it will be extremely hard to define their positions relative to one another. Thus, one cannot overemphasize the advantage of the approach of this study. The porosity equal to zero is analogous to inclusion concentration equal to zero, at which point the rock is strongest. Thus, this study can be useful when engineers aim to quantify the effect of natural fractures (inclusions) on the bulk modulus during drilling waste injection, for example. For such processes, the zones with a large number of natural fractures will possess lower elastic stiffness.

The wireline log for the Nembe Well-X: Cal is caliper (1 in. = 0.0254 m), Den is bulk density (1 g/cc = 1000 kg/m3), TT is transit time (1 μs/ft = 3.28 μs/m), and GR is gamma ray (API). For depth, 1 ft = 0.3047 m.
The following section compares the model developed in this study with the conventional models for predicting the effective bulk modulus. Using the data in Fig. 2, the models of Voigt, Hashin and Shtrikman, Reuss, and Voigt–Reuss–Hill were used to calculate the bulk modulus. The plots of the bulk modulus against porosity are shown in Fig. 3. The values obtained are between Voigt and Ruess’ models, over the range of the porosity considered, except at the boundaries where all the models converge. The study model (K model) yielded results between the upper and lower bounds and was compared favorably with the HS model. If the extra fluids in the microcracks were not incorporated into the analysis, the study model would yield values similar to Voigt’s upper bound. The distribution of the models under investigation does not follow the same path. This is because of differences in the underlying principles that form their basis. The Voigt model indicates a more linear path than the others as indicated in Fig. 3, while the Ruess’ model dropped significantly because of its tendency towards the bulk modulus of the fluid. If the difference between the modulus of the solid and fluid is narrow, the Ruess’ model will converge towards the other results.
NB: it is more appropriate to use regional or local data to obtain the parameter for any numerical scheme. Figure 5 shows how the study model compares with the numerical model, which was obtained using the average bulk modulus of the selected models.
Note that when the value of the parameter (x) equals unity, one obtains Voigt’s bulk modulus. However, with the use of the data of this study and the numerical scheme, the value of the parameter (x) is 0.8228. Like before, one can apply regional or local data to get a more accurate value for the parameter. Figure 10 is a comparison of the modified frame porosity and the other models. The modified frame model is very close to the average of the HS and VRH models.
Equations (28), (36), and (46) will most likely yield similar results when the same set of local data is used to obtain the parameters. The Voigt and Reuss models that follow the concept of deformation of layers yield different results most probably because they do not consider the deformation of microcracks. The rate of change of the bulk modulus with porosity depends on the difference between the fluid and solid bulk moduli. This is observed when one differentiates the bulk modulus with respect to porosity. For example, differentiating Eqs. (28), (36), and (46) with respect to porosity shows that the analytical method has the largest recovery of the bulk modulus compared to the numerical and modified frame models.
The accuracy between these values (over 99%) is an indication that the crack porosity is accurate, yields representative result, and constitutes the major part of the secondary porosity in this wellbore. NB: fracture porosity is commonly lower than 1% of bulk volume in carbonates or clastic rocks [24]. Higher porosity will yield higher crack porosity using Eq. (32).
Conclusion
The goal of this study was to develop a means to predict the bulk modulus of rocks based on the effective medium theory. One objective was to overcome the limitation of conventional models, which do not include the deformation of microcracks. This was achieved by accounting for the volume of fluids in the microcracks and using the effective medium theory. The FEA and petroleum industry equations were used to validate the models developed in this study, and the results of the bulk modulus lie between the bounds of the Voigt and Reuss models. The following points are drawn from the study:
The addition of the fluids in microcracks yields results lower than the Voigt model but higher than the Reuss model.
The presence of microcracks/inclusions reduces the effective bulk modulus of the rock.
When the secondary porosity is mainly from natural cracks, higher primary porosity corresponds to higher crack porosity.
The rate of change of bulk modulus with porosity is higher when one considers the fluids in the microcracks.
The magnitude and concentration of inclusions significantly affect how rocks respond to external stresses.
The critical porosity approach is a sophisticated way of predicting the effective bulk modulus when the boundary condition for fluids is incorporated.
Acknowledgment
Thanks go to Dr. Abrakasa of the Department of Geology, University of Port Harcourt for providing the data from where the authors plotted Fig. 1. Stipends from FUW helped in carrying out the research.
Conflict of Interest
There are no conflicts of interest. This article does not include research in which human participants were involved. Informed consent is not applicable. This article does not include any research in which animal participants were involved.
Data Availability Statement
The authors attest that all data for this study are included in the paper.